Figure 1c - Which printing methods are used which which bioink?

Interactive sankey diagram - hover over each section to see the number of experiments in each category.

library(plotly)
## 
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
## 
##     last_plot
## The following object is masked from 'package:stats':
## 
##     filter
## The following object is masked from 'package:graphics':
## 
##     layout
#### prep data for sankey diagram
# type --> 
Print_bioink <- reconciled %>%select(
  study_ID, 
  `4.1 What kind of printing method is used?_ae684e28-4f90-44ad-9de2-731d076de0b0_Answer`,
  `4.1.3 What type of bioink was used?_92421414-c597-4e9c-b720-9bb4318b5483_Answer`, 
  `4.1.3.1.1 If natural bioink, please choose the type._66b761ad-6630-4323-b423-c848a717aae4_Answer`, 
  `4.1.3.1.4 If Synthetic bioink: choose the type _e74ea0e0-a654-4ae2-b59e-2e0b14a4651a_Answer`, 
  `4.1.3.1.2 If Protein based: choose the type._621671b3-9f7e-4a22-80ac-14b02fdd0683_Answer`, 
  `4.1.3.1.3 If Polysaccharides Based: choose the type. _fcea8bcb-da6c-4154-aa44-8ff4734fa4fe_Answer`
)  


Print_bioink_re <- Print_bioink  %>% rename(
  print_method = `4.1 What kind of printing method is used?_ae684e28-4f90-44ad-9de2-731d076de0b0_Answer`,
  typeGeneral_level1 = `4.1.3 What type of bioink was used?_92421414-c597-4e9c-b720-9bb4318b5483_Answer`, 
  typeNatural_level2 = `4.1.3.1.1 If natural bioink, please choose the type._66b761ad-6630-4323-b423-c848a717aae4_Answer`, 
  typeSynthetic_level2 = `4.1.3.1.4 If Synthetic bioink: choose the type _e74ea0e0-a654-4ae2-b59e-2e0b14a4651a_Answer`, 
  typeProtein_level3 = `4.1.3.1.2 If Protein based: choose the type._621671b3-9f7e-4a22-80ac-14b02fdd0683_Answer`, 
  typePoly_level3 = `4.1.3.1.3 If Polysaccharides Based: choose the type. _fcea8bcb-da6c-4154-aa44-8ff4734fa4fe_Answer`
)


#### needs to be 72 rows only!! - MH will supply which combo bioInk to Printer is correct from original study 
# 487b3d85-d671-40f2-8d60-309a6aa9a4f3

# Print_bioink_re <- within(Print_bioink_re, print_method[print_method == "Extrusion based|Stereolithography" & study_ID == '487b3d85-d671-40f2-8d60-309a6aa9a4f3'] <- 'John Smith1')

Print_bioink_re$print_method <- as.character(Print_bioink_re$print_method)

Print_bioink_re <- Print_bioink_re %>% mutate(print_method = ifelse(study_ID == '487b3d85-d671-40f2-8d60-309a6aa9a4f3', "Extrusion based", print_method))

# Print_bioink_re[11, "print_method"] <- "Extrusion based"



Print_bioink1_1 <- Print_bioink_re %>% 
                separate_rows(print_method , sep="\\|")


Print_bioink1_2 <- Print_bioink_re %>%
             separate_rows(typeGeneral_level1, sep="\\|")

Print_bioink1_2$print_method <- as.factor(Print_bioink1_2$print_method)
Print_bioink1_2$typeGeneral_level1 <- as.factor(Print_bioink1_2$typeGeneral_level1 )
  

#   487b3d85-d671-40f2-8d60-309a6aa9a4f3
# extrusion was 
# sterolithography



# summary(Print_bioink1)

Print_bioink1_sankey <- Print_bioink1_2 %>% select(study_ID, print_method, typeGeneral_level1)

Print_bioink1_sankey
## # A tibble: 72 × 3
##    study_ID                             print_method    typeGeneral_level1
##    <chr>                                <fct>           <fct>             
##  1 0b78f899-524b-49df-8b02-bdb64e281ed4 Inject based    Natural           
##  2 f0faaae5-eba2-4213-afe0-d958d5279121 Extrusion based Natural           
##  3 f0faaae5-eba2-4213-afe0-d958d5279121 Extrusion based Synthetic         
##  4 487b3d85-d671-40f2-8d60-309a6aa9a4f3 Extrusion based Natural           
##  5 487b3d85-d671-40f2-8d60-309a6aa9a4f3 Extrusion based Synthetic         
##  6 d21360df-70ad-426a-b86e-b6eccebcbe11 Extrusion based Natural           
##  7 11abf440-c03a-4830-9c5d-4e3688fe8808 Other           Natural           
##  8 6955f074-b9f4-4f46-b6a8-06b49f150239 Inject based    Unclear           
##  9 13e55d40-f8a1-43bd-98ca-32db20c905cd Other           Natural           
## 10 2a8a7137-e063-43ca-aeaa-f6b69913cfd4 Extrusion based Synthetic         
## # ℹ 62 more rows
# create a table of frequencies
freq_table <- Print_bioink1_sankey %>% group_by(print_method, typeGeneral_level1) %>% 
  summarise(n = n())
## `summarise()` has grouped output by 'print_method'. You can override using the
## `.groups` argument.
freq_table <- freq_table %>% 
   mutate(print_method= recode(print_method, "Extrusion based" = "Extrusion")) %>% 
     mutate(print_method= recode(print_method, "Unclear" = "Unclear - Method")) %>% 
  mutate(print_method= recode(print_method, "Inject based" = "Injection")) %>% 
     mutate(typeGeneral_level1= recode(typeGeneral_level1, "Unclear" = "Unclear - Bioink"))


# create a nodes data frame
nodes <- data.frame(name = unique(c(as.character(freq_table$print_method),
                                    as.character(freq_table$typeGeneral_level1))))


# create links dataframe
links <- data.frame(source = match(freq_table$print_method, nodes$name) - 1,
                    target = match(freq_table$typeGeneral_level1, nodes$name) - 1,
                    value = freq_table$n,
                    stringsAsFactors = FALSE)



# Make Sankey diagram
plot_ly(
  type = "sankey",
  orientation = "h",
  node = list(pad = 15,
              thickness = 20,
              line = list(color = "black", width = 0.5),
              label = nodes$name),
  link = list(source = links$source,
              target = links$target,
              value = links$value),
  textfont = list(size = 18, color = "black"),
  width = 820,
  height = 580
) %>%
  layout(
    #title = "Sankey Diagram: Print Method & Ink Type",
         font = list(size = 18),
         margin = list(t = 40, l = 10, r = 10, b = 10))

Figure 2a - What bioinks were used?

Interactive Sunburst plot - hover over each section to see the number of experiments in each category.

#### BIOINK sunburst 

library(dplyr)

# type --> 
bioink <- reconciled %>% select(
  study_ID, 
    `4.1.3 What type of bioink was used?_92421414-c597-4e9c-b720-9bb4318b5483_Answer`, 
    `4.1.3.1.1 If natural bioink, please choose the type._66b761ad-6630-4323-b423-c848a717aae4_Answer`, 
    `4.1.3.1.4 If Synthetic bioink: choose the type _e74ea0e0-a654-4ae2-b59e-2e0b14a4651a_Answer`, 
  `4.1.3.1.2 If Protein based: choose the type._621671b3-9f7e-4a22-80ac-14b02fdd0683_Answer`, 
`4.1.3.1.3 If Polysaccharides Based: choose the type. _fcea8bcb-da6c-4154-aa44-8ff4734fa4fe_Answer`
  )  

# reconciled$`4.1.3.1.4 If Synthetic bioink: choose the type _e74ea0e0-a654-4ae2-b59e-2e0b14a4651a_Answer`

bioink <- bioink  %>% rename(typeGeneral_level1 = `4.1.3 What type of bioink was used?_92421414-c597-4e9c-b720-9bb4318b5483_Answer`, 
   typeNatural_level2 = `4.1.3.1.1 If natural bioink, please choose the type._66b761ad-6630-4323-b423-c848a717aae4_Answer`, 
  typeSynthetic_level2 = `4.1.3.1.4 If Synthetic bioink: choose the type _e74ea0e0-a654-4ae2-b59e-2e0b14a4651a_Answer`, 
  typeProtein_level3 = `4.1.3.1.2 If Protein based: choose the type._621671b3-9f7e-4a22-80ac-14b02fdd0683_Answer`, 
  typePoly_level3 = `4.1.3.1.3 If Polysaccharides Based: choose the type. _fcea8bcb-da6c-4154-aa44-8ff4734fa4fe_Answer`
)



### other is Matrigel
#################### update after discussion with experts on interpretation
bioink_split1 <- separate_rows(bioink, typeGeneral_level1 , sep="\\|")

### fill in NAs 
# turn all cols to character
bioInk_72 <- bioink_split1 %>%
  mutate(across(everything(), as.character))

bioInk_72 <- bioink_split1 %>%
     mutate(
typeNatural_level2 = replace(typeNatural_level2, typeGeneral_level1!="Natural", NA), 
typeSynthetic_level2 = replace(typeSynthetic_level2, typeGeneral_level1!="Synthetic", NA), 
typeProtein_level3 = replace(typeProtein_level3, typeGeneral_level1!="Natural", NA), 
typePoly_level3 =  replace(typePoly_level3, typeGeneral_level1!="Natural", NA),

#typePoly_level3 =  replace(typePoly_level3, typeNatural_level2!="Polysaccharide based", NA), 
#typeProtein_level3 = replace(typeProtein_level3, typeNatural_level2!="Protein based", NA), 
# typePoly_level3 =  replace(typePoly_level3, typeGeneral_level1!="Natural", NA)
            )


 ##### level 2 
 bioInk_72$level2 <- apply(bioInk_72[, c("typeNatural_level2", "typeSynthetic_level2")], 1, 
                           function(i){ paste(na.omit(i), collapse = " ") })

 ##### level 3 
 bioInk_72$level3 <- apply(bioInk_72[, c("typeProtein_level3", "typePoly_level3")], 1,
                       function(i){ paste(na.omit(i), collapse = " ") })



#### rename the columns 
# rename answers to more easily group them
bioInk_73 <- bioInk_72 %>%
      mutate(level3 = recode(level3, "Collagens" = "Collagen")) %>%
      mutate(level3 = recode(level3, "Collagens;Gelatin" = "Collagen+Gelatin")) %>%
      mutate(level3 = recode(level3, "Collagens|Collagens" = "Collagen")) %>%
      mutate(level3 = recode(level3, "Gelatin|Gelatin" = "Gelatin")) %>%
      mutate(level3 = recode(level3, "Gelatin;Silk-fibroin" = "Gelatin+Silk-fibroin")) %>%
      mutate(level3 = recode(level3, "Gelatin;Fibrinogen" = "Gelatin+Fibrinogen")) %>%
  
  mutate(level3 = recode(level3, "Collagens;Gelatin Alginates" = "Collagen+Gelatin+Alginates")) %>%
  mutate(level3 = recode(level3, "Alginates;Other" = "Alginates+Other")) %>%
  mutate(level3 = recode(level3, "Gelatin Alginates" = "Gelatin+Alginates")) %>%
  mutate(level3 = recode(level3, "Collagens Alginates" = "Collagen+Alginates")) %>%
  mutate(level3 = recode(level3, "Fibrinogen Alginates" = "Fibrinogen+Alginates")) %>%
  
  mutate(level3 = recode(level3, "Gelatin Alginates;Other" = "Gelatin+Alginates+Other")) %>%
  mutate(level3 = recode(level3, "Fibrinogen;Other Alginates;Other" = "Fibrinogen+Alginates+Other")) %>%
  mutate(level3 = recode(level3, "Collagens|Collagens Hyaluronic acid|Hyaluronic acid" = "Collagen+Hyaluronic acid")) %>%
  mutate(level3 = recode(level3, "Gelatin;Other Alginates" = "Gelatin+Alginates+Other")) %>%
  
  mutate(level3 = recode(level3, "Gelatin;Other Alginates;Other" = "Gelatin+Alginates+Other")) %>%
  mutate(level3 = recode(level3, "Gelatin Chitosan" = "Gelatin+Chitosan")) %>%
  mutate(level3 = recode(level3, "Gelatin Hyaluronic acid" = "Gelatin+Hyaluronic acid")) %>%
      mutate(level2 = recode(level2, "Poly ethylene glycol (PEG)" = "PEG")) %>% 
      mutate(level2 = recode(level2, "Protein based" = "Protein")) %>% 
      mutate(level2 = recode(level2, "Polysaccharide based" = "Poly")) %>% 
      mutate(level2 = recode(level2, "dECM based" = "dECM")) %>% 
      mutate(level2 = recode(level2, "Protein based;Polysaccharide based" = "Protein+Poly")) %>% 
mutate(level2 = recode(level2, "Protein based;dECM based" = "Protein+dECM")) %>% 
mutate(level2 = recode(level2, "Polysaccharide based;dECM based" = "Poly+dECM")) %>% 
      mutate(level2 = recode(level2, "Protein based;Polysaccharide based;Other" = "Protein+Poly+MatriGel")) %>% 
      mutate(level2 = recode(level2, "Protein based;Polysaccharide based;dECM based" = "Protein+Poly+dECM")) %>% 
      mutate(level2 = recode(level2, "Protein based;Polysaccharide based|Protein based;Polysaccharide based" = "Protein+Poly")) %>% 
      mutate(level2 = recode(level2, "Protein based|Protein based" = "Protein")) %>% 
      mutate(typeGeneral_level1 = recode(typeGeneral_level1, "Not reported" = "NR")        
             )
  
#  Protein based|dECM based
# which(bioInk_72$level2 == "Protein based|dECM based")

# bioInk_72[13, "level2"] <- "Protein based"
# bioInk_72[14, "level2"] <- "dECM based"


bioInk_73[30, "level2"] <- "Protein"
bioInk_73[31, "level2"] <- "dECM "


# other in "not reported" should be removed

#### START PLOTTING

library(dplyr)
library(plotme)
library(plotly)
library(palmerpenguins)

# Create the plots
fig_bioInk_simple <- bioInk_73 %>% 
  count(typeGeneral_level1, level2) %>% 
  plotme::count_to_sunburst()

# Update the colors
fig_bioInk_simple %>% 
    plotly::style(marker = list(colors = c("#a6611a","#dfc27d", "#80cdc1", "#018571"))) 
bioInk_count_suppl <-  bioInk_73 %>%  count(
                       typeGeneral_level1, 
                       level2
                        , level3
                       #,study_ID
                       )%>% 
  plotme::count_to_sunburst()



bioInk_count_suppl %>% 
    plotly::style(marker = list(colors = c("#a6611a","#dfc27d", "#80cdc1", "#018571"))) 
# bioInk_count_simple <-  count(bioInk_73, 
#                        typeGeneral_level1, 
#                        level2
#                        # , level3
#                        #,study_ID
#                        )

# # sunburst plot
# plotme::count_to_sunburst(bioInk_count_simple
#                           
#                           ,sort_by_n = TRUE 
#                           
#                           # ,fill_by_n = TRUE
# 
#                                             )

# link to raw function code: https://github.com/yogevherz/plotme/blob/master/R/count_to_sunburst_treemap.R 

# plotme::count_to_sunburst(bioInk_count_suppl
#                           
#                           ,sort_by_n = TRUE 
#                           
#                           # ,fill_by_n = TRUE
#                           )

Figure 2a - version2 - What bioinks were used?

Interactive Sunburst plot - hover over each section to see the number of experiments in each category.

#### BIOINK sunburst 

library(dplyr)

# type --> 
bioink <- reconciled %>% select(
  study_ID, 
    `4.1.3 What type of bioink was used?_92421414-c597-4e9c-b720-9bb4318b5483_Answer`, 
    `4.1.3.1.1 If natural bioink, please choose the type._66b761ad-6630-4323-b423-c848a717aae4_Answer`, 
    `4.1.3.1.4 If Synthetic bioink: choose the type _e74ea0e0-a654-4ae2-b59e-2e0b14a4651a_Answer`, 
  `4.1.3.1.2 If Protein based: choose the type._621671b3-9f7e-4a22-80ac-14b02fdd0683_Answer`, 
`4.1.3.1.3 If Polysaccharides Based: choose the type. _fcea8bcb-da6c-4154-aa44-8ff4734fa4fe_Answer`
  )  

# reconciled$`4.1.3.1.4 If Synthetic bioink: choose the type _e74ea0e0-a654-4ae2-b59e-2e0b14a4651a_Answer`

bioink <- bioink  %>% rename(typeGeneral_level1 = `4.1.3 What type of bioink was used?_92421414-c597-4e9c-b720-9bb4318b5483_Answer`, 
   typeNatural_level2 = `4.1.3.1.1 If natural bioink, please choose the type._66b761ad-6630-4323-b423-c848a717aae4_Answer`, 
  typeSynthetic_level2 = `4.1.3.1.4 If Synthetic bioink: choose the type _e74ea0e0-a654-4ae2-b59e-2e0b14a4651a_Answer`, 
  typeProtein_level3 = `4.1.3.1.2 If Protein based: choose the type._621671b3-9f7e-4a22-80ac-14b02fdd0683_Answer`, 
  typePoly_level3 = `4.1.3.1.3 If Polysaccharides Based: choose the type. _fcea8bcb-da6c-4154-aa44-8ff4734fa4fe_Answer`
)



### other is Matrigel
#################### update after discussion with experts on interpretation
bioink_split1 <- separate_rows(bioink, typeGeneral_level1 , sep="\\|")

### fill in NAs 
# turn all cols to character
bioInk_72 <- bioink_split1 %>%
  mutate(across(everything(), as.character))

bioInk_72 <- bioink_split1 %>%
     mutate(
typeNatural_level2 = replace(typeNatural_level2, typeGeneral_level1!="Natural", NA), 
typeSynthetic_level2 = replace(typeSynthetic_level2, typeGeneral_level1!="Synthetic", NA), 
typeProtein_level3 = replace(typeProtein_level3, typeGeneral_level1!="Natural", NA), 
typePoly_level3 =  replace(typePoly_level3, typeGeneral_level1!="Natural", NA),

#typePoly_level3 =  replace(typePoly_level3, typeNatural_level2!="Polysaccharide based", NA), 
#typeProtein_level3 = replace(typeProtein_level3, typeNatural_level2!="Protein based", NA), 
# typePoly_level3 =  replace(typePoly_level3, typeGeneral_level1!="Natural", NA)
            )


 ##### level 2 
 bioInk_72$level2 <- apply(bioInk_72[, c("typeNatural_level2", "typeSynthetic_level2")], 1, 
                           function(i){ paste(na.omit(i), collapse = " ") })

 ##### level 3 
 bioInk_72$level3 <- apply(bioInk_72[, c("typeProtein_level3", "typePoly_level3")], 1,
                       function(i){ paste(na.omit(i), collapse = " ") })



#### rename the columns 
# rename answers to more easily group them
bioInk_73 <- bioInk_72 %>%
      mutate(level3 = recode(level3, "Collagens" = "Collagen")) %>%
      mutate(level3 = recode(level3, "Collagens;Gelatin" = "Collagen+Gelatin")) %>%
      mutate(level3 = recode(level3, "Collagens|Collagens" = "Collagen")) %>%
      mutate(level3 = recode(level3, "Gelatin|Gelatin" = "Gelatin")) %>%
      mutate(level3 = recode(level3, "Gelatin;Silk-fibroin" = "Gelatin+Silk-fibroin")) %>%
      mutate(level3 = recode(level3, "Gelatin;Fibrinogen" = "Gelatin+Fibrinogen")) %>%
  
  mutate(level3 = recode(level3, "Collagens;Gelatin Alginates" = "Collagen+Gelatin+Alginates")) %>%
  mutate(level3 = recode(level3, "Alginates;Other" = "Alginates+Other")) %>%
  mutate(level3 = recode(level3, "Gelatin Alginates" = "Gelatin+Alginates")) %>%
  mutate(level3 = recode(level3, "Collagens Alginates" = "Collagen+Alginates")) %>%
  mutate(level3 = recode(level3, "Fibrinogen Alginates" = "Fibrinogen+Alginates")) %>%
  
  mutate(level3 = recode(level3, "Gelatin Alginates;Other" = "Gelatin+Alginates+Other")) %>%
  mutate(level3 = recode(level3, "Fibrinogen;Other Alginates;Other" = "Fibrinogen+Alginates+Other")) %>%
  mutate(level3 = recode(level3, "Collagens|Collagens Hyaluronic acid|Hyaluronic acid" = "Collagen+Hyaluronic acid")) %>%
  mutate(level3 = recode(level3, "Gelatin;Other Alginates" = "Gelatin+Alginates+Other")) %>%
  
  mutate(level3 = recode(level3, "Gelatin;Other Alginates;Other" = "Gelatin+Alginates+Other")) %>%
  mutate(level3 = recode(level3, "Gelatin Chitosan" = "Gelatin+Chitosan")) %>%
  mutate(level3 = recode(level3, "Gelatin Hyaluronic acid" = "Gelatin+Hyaluronic acid")) %>%
      mutate(level2 = recode(level2, "Poly ethylene glycol (PEG)" = "PEG")) %>% 
      mutate(level2 = recode(level2, "Protein based" = "Protein")) %>% 
      mutate(level2 = recode(level2, "Polysaccharide based" = "Polysaccharide")) %>% 
      mutate(level2 = recode(level2, "dECM based" = "dECM")) %>% 
      mutate(level2 = recode(level2, "Protein based;Polysaccharide based" = "Protein+Polysaccharide")) %>% 
mutate(level2 = recode(level2, "Protein based;dECM based" = "Protein+dECM")) %>% 
mutate(level2 = recode(level2, "Polysaccharide based;dECM based" = "Polysaccharide+dECM")) %>% 
      mutate(level2 = recode(level2, "Protein based;Polysaccharide based;Other" = "Protein+Polysaccharide+MatriGel")) %>% 
      mutate(level2 = recode(level2, "Protein based;Polysaccharide based;dECM based" = "Protein+Polysaccharide+dECM")) %>% 
      mutate(level2 = recode(level2, "Protein based;Polysaccharide based|Protein based;Polysaccharide based" = "Protein+Polysaccharide")) %>% 
      mutate(level2 = recode(level2, "Protein based|Protein based" = "Protein")) %>% 
      mutate(typeGeneral_level1 = recode(typeGeneral_level1, "Not reported" = "NR")        
             )
  
#  Protein based|dECM based
# which(bioInk_72$level2 == "Protein based|dECM based")

# bioInk_72[13, "level2"] <- "Protein based"
# bioInk_72[14, "level2"] <- "dECM based"


bioInk_73[30, "level2"] <- "Protein"
bioInk_73[31, "level2"] <- "dECM "


# other in "not reported" should be removed

#### START PLOTTING

library(dplyr)
library(plotme)
library(plotly)
library(palmerpenguins)

# Create the plots
fig_bioInk_simple <- bioInk_73 %>% 
  count(typeGeneral_level1, level2) %>% 
  plotme::count_to_sunburst()

# Update the colors
fig_bioInk_simple %>% 
    plotly::style(marker = list(colors = c("#a6611a","#dfc27d", "#80cdc1", "#018571"))) 
bioInk_count_suppl <-  bioInk_73 %>%  count(
                       typeGeneral_level1, 
                       level2
                        , level3
                       #,study_ID
                       )%>% 
  plotme::count_to_sunburst()



bioInk_count_suppl %>% 
    plotly::style(marker = list(colors = c("#a6611a","#dfc27d", "#80cdc1", "#018571"))) 
# bioInk_count_simple <-  count(bioInk_73, 
#                        typeGeneral_level1, 
#                        level2
#                        # , level3
#                        #,study_ID
#                        )

# # sunburst plot
# plotme::count_to_sunburst(bioInk_count_simple
#                           
#                           ,sort_by_n = TRUE 
#                           
#                           # ,fill_by_n = TRUE
# 
#                                             )

# link to raw function code: https://github.com/yogevherz/plotme/blob/master/R/count_to_sunburst_treemap.R 

# plotme::count_to_sunburst(bioInk_count_suppl
#                           
#                           ,sort_by_n = TRUE 
#                           
#                           # ,fill_by_n = TRUE
#                           )